Introduction to Data Analysis and Visualization with R

June 27 & 29, 2023

Deadline to register is June 16, 2023 at 5:00 pm Pacific (late registrations may be accepted if space is available). Workshop capacity is limited to 20 attendees; Zoom link and additional details will be sent with confirmation of acceptance via email by June 20, 2023.  

Participants will learn basic coding skills in R and RStudio to perform exploratory data analyses and how to plot results graphically using the programming language and sample data sets from ecological and environmental science applications. 

There are no prerequisites for this workshop, however, participants may be required to download free open-source software and example datasets to complete the lessons presented. Participants are also encouraged to bring some of their own data to practice with as time allows; instructions and FAQs will be provided with confirmation of acceptance. 

Part 1: Tuesday, June 27th, 10am-2pm Pacific Time
Part 2: Thursday, June 29th, 10am-2pm Pacific Time

Topics include: 

  • Overview of R, R Studio, terminology, basic coding, and best practices

  • Cleaning, manipulating and analyzing data 

  • Making charts, plots, maps, and tables
  • Exploratory data analysis

The 2-part format includes initial sessions for lessons and demonstrations, complemented by monthly “Data Drop-in Sessions” specifically for tribal participants to ask their individual data management questions and find direct assistance for their needs. Live sessions will not be recorded; participants will have access to lesson materials online as a reference.

This workshop is intended for tribal professionals managing, assessing, and reporting data for ecological and environmental monitoring projects, with examples from real-world scenarios in air quality, water quality, land management, wildlife management, etc. Participants are not required to have prior experience with the tools presented, but a general understanding of working with data in different forms (spreadsheets, databases, etc.) and an enthusiasm to learn basic R-coding skills are highly recommended.